DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials Article Swipe

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Wenfei Li , Qi Ou , Yixiao Chen , Yu Cao , Renxi Liu , Chunyi Zhang , Daye Zheng , Chun Cai , Xifan Wu , Han Wang , Mohan Chen , Linfeng Zhang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1021/acs.jpca.2c05000

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

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Computer Science

Quantum

Monte Carlo Method

Artificial Intelligence

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Computer science Bridge (graph theory) Density functional theory Quantum Abacus (architecture) Monte Carlo method Quantum Monte Carlo Construct (python library) Artificial intelligence Algorithm Chemistry Mathematics Computational chemistry Physics Quantum mechanics Archaeology Statistics History Medicine Programming language Internal medicine

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Type: article
Language: en
Landing Page: https://doi.org/10.1021/acs.jpca.2c05000
OA Status: green
Cited By: 13
References: 60
Related Works: 10
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DOI: https://doi.org/10.1021/acs.jpca.2c05000

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Title: DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

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Language: en

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Publication year: 2022

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Publication date: 2022-12-01

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Authors: Wenfei Li, Qi Ou, Yixiao Chen, Yu Cao, Renxi Liu, Chunyi Zhang, Daye Zheng, Chun Cai, Xifan Wu, Han Wang, Mohan Chen, Linfeng Zhang

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Landing page: https://doi.org/10.1021/acs.jpca.2c05000

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OA URL: https://www.osti.gov/biblio/1994169

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Concepts: Computer science, Bridge (graph theory), Density functional theory, Quantum, Abacus (architecture), Monte Carlo method, Quantum Monte Carlo, Construct (python library), Artificial intelligence, Algorithm, Chemistry, Mathematics, Computational chemistry, Physics, Quantum mechanics, Archaeology, Statistics, History, Medicine, Programming language, Internal medicine

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Cited by: 13

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